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RandomAnisotropy

Simulation of an image with highly anisotropic spacing

RandomAnisotropy

Bases: RandomTransform

Downsample an image along an axis and upsample to initial space.

This transform simulates an image that has been acquired using anisotropic spacing and resampled back to its original spacing.

Similar to the work by Billot et al.: Partial Volume Segmentation of Brain MRI Scans of any Resolution and Contrast .

Parameters:

Name Type Description Default
axes int | tuple[int, ...]

Axis or tuple of axes along which the image will be downsampled.

 (0, 1, 2)
downsampling TypeRangeFloat

Downsampling factor \(m \gt 1\). If a tuple \((a, b)\) is provided then \(m \sim \mathcal{U}(a, b)\).

 (1.5, 5)
image_interpolation str

Image interpolation used to upsample the image back to its initial spacing. Downsampling is performed using nearest neighbor interpolation. See Interpolation for supported interpolation types.

 'linear'
scalars_only bool

Apply only to instances of torchio.ScalarImage. This is useful when the segmentation quality needs to be kept, as in Billot et al. .

 True
**kwargs

See Transform for additional keyword arguments.

 {}

Examples:

>>> import torchio as tio
>>> transform = tio.RandomAnisotropy(axes=1, downsampling=2)
>>> transform = tio.RandomAnisotropy(
...     axes=(0, 1, 2),
...     downsampling=(2, 5),
... )   # Multiply spacing of one of the 3 axes by a factor randomly chosen in [2, 5]
>>> colin = tio.datasets.Colin27()
>>> transformed = transform(colin)

__call__(data) 

__call__(data: Subject) -> Subject

__call__(data: ImageT) -> ImageT

__call__(data: torch.Tensor) -> torch.Tensor

__call__(data: np.ndarray) -> np.ndarray

__call__(data: sitk.Image) -> sitk.Image

__call__(data: dict[str, object]) -> dict[str, object]

__call__(data: nib.Nifti1Image) -> nib.Nifti1Image

Transform data and return a result of the same type.

Parameters:

Name Type Description Default
data TypeTransformInput

Instance of torchio.Subject, 4D torch.Tensor or numpy.ndarray with dimensions \((C, W, H, D)\), where \(C\) is the number of channels and \(W, H, D\) are the spatial dimensions. If the input is a tensor, the affine matrix will be set to identity. Other valid input types are a SimpleITK image, a torchio.Image, a NiBabel Nifti1 image or a dict. The output type is the same as the input type.

 required

to_hydra_config()

Return a dictionary representation of the transform for Hydra instantiation.